clustermole_enrichment: Cell types based on the expression of all genes

In clustermole: Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Description

Perform enrichment of cell type signatures based on the full gene expression matrix.

Usage

clustermole_enrichment(expr_mat, species, method = "gsva")

Arguments

expr_mat	Expression matrix (logCPMs, logFPKMs, or logTPMs) with genes as rows and clusters/populations/samples as columns.
species	Species: hs for human or mm for mouse.
method	Enrichment method: ssgsea, gsva, singscore, or all. The method to use for the estimation of gene set enrichment scores. The options are ssGSEA (Barbie et al, 2009), GSVA (Hänzelmann et al, 2013), singscore (Foroutan et al, 2018), or a combination of all three methods.

Value

A data frame of enrichment results.

References

Barbie, D., Tamayo, P., Boehm, J. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009). doi: 10.1038/nature08460

Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013). doi: 10.1186/1471-2105-14-7

Foroutan, M., Bhuva, D.D., Lyu, R. et al. Single sample scoring of molecular phenotypes. BMC Bioinformatics 19, 404 (2018). doi: 10.1186/s12859-018-2435-4

Examples

# my_enrichment <- clustermole_enrichment(expr_mat = my_expr_mat, species = "hs")

clustermole documentation built on Jan. 26, 2021, 9:05 a.m.